scholarly journals The Recognition and Interactional Management of Face Threats: Comparing Neurotypical Participants and Participants with Asperger's Syndrome

2021 ◽  
pp. 019027252110030
Author(s):  
Emmi Koskinen ◽  
Melisa Stevanovic ◽  
Anssi Peräkylä
Keyword(s):  
Autism Spectrum Disorder ◽  
Conversation Analysis ◽  
Asperger's Syndrome ◽  
Asperger’S Syndrome ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Data Set ◽  
Erving Goffman ◽  
Face Threats

Erving Goffman has argued that the threat of losing one's face is an omnirelevant concern that penetrates all actions in encounters. However, studies have shown that compared with neurotypical individuals, persons diagnosed with autism spectrum disorder can be less preoccupied with how others perceive them and thus possibly less concerned of face in interaction. Drawing on a data set of Finnish quasinatural conversations, we use the means of conversation analysis to compare the practices of facework in storytelling sequences involving neurotypical (NT) participants and participants diagnosed with Asperger's syndrome (AS). We found differences in the ways in which the AS and NT participants in our data managed face threats in interaction, where they spontaneously assumed the roles of both storytellers and story recipients. We discuss our findings in relation to theories of self in interaction, with an aim to illuminate both typical and atypical interactional practices of facework.

Zena Hamelson

2018 ◽  
Author(s):  
Michael B. Bakan
Keyword(s):  
Autism Spectrum Disorder ◽  
Asperger's Syndrome ◽  
Asperger’S Syndrome ◽  
Social Experience ◽  
Autism Spectrum ◽  
Creative Expression ◽  
Spectrum Disorder ◽  
Therapeutic Interventions ◽  
Music Making ◽  
The Brain

When we first meet ten-year-old Zena Hamelson, she is sitting in a chair staring blankly at the wall, flapping her hands, repeatedly straightening and bending her legs, compulsively twisting and pulling on her fingers as her Artism Ensemble bandmates make joyful music all around her. Zena is stimming, that is, she is practicing a personal repertoire of self-stimulatory behaviors that align precisely with the symptomatic profile of her diagnosed autism spectrum disorder: Asperger’s syndrome. Stimming, autism researchers tell us, is associated with some dysfunctional system in the brain; its reduction or elimination is a target goal of many therapeutic interventions and autism studies. Yet as the chapter unfolds, Zena’s stimming is revealed as something else entirely: a meaningful mode of music-making, creative expression, and social experience unto itself.

scholarly journals Deep-Learning-Based Detection of Infants with Autism Spectrum Disorder Using Auto-Encoder Feature Representation

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6762
Author(s):  
Jung Hyuk Lee ◽  
Geon Woo Lee ◽  
Guiyoung Bong ◽  
Hee Jeong Yoo ◽  
Hong Kook Kim
Keyword(s):  
Autism Spectrum Disorder ◽  
Feature Extraction ◽  
Deep Learning ◽  
Autism Spectrum ◽  
Feature Representation ◽  
Diagnostic Methods ◽  
Spectrum Disorder ◽  
Joint Optimization ◽  
Data Set ◽  
Children With Asd

Autism spectrum disorder (ASD) is a developmental disorder with a life-span disability. While diagnostic instruments have been developed and qualified based on the accuracy of the discrimination of children with ASD from typical development (TD) children, the stability of such procedures can be disrupted by limitations pertaining to time expenses and the subjectivity of clinicians. Consequently, automated diagnostic methods have been developed for acquiring objective measures of autism, and in various fields of research, vocal characteristics have not only been reported as distinctive characteristics by clinicians, but have also shown promising performance in several studies utilizing deep learning models based on the automated discrimination of children with ASD from children with TD. However, difficulties still exist in terms of the characteristics of the data, the complexity of the analysis, and the lack of arranged data caused by the low accessibility for diagnosis and the need to secure anonymity. In order to address these issues, we introduce a pre-trained feature extraction auto-encoder model and a joint optimization scheme, which can achieve robustness for widely distributed and unrefined data using a deep-learning-based method for the detection of autism that utilizes various models. By adopting this auto-encoder-based feature extraction and joint optimization in the extended version of the Geneva minimalistic acoustic parameter set (eGeMAPS) speech feature data set, we acquire improved performance in the detection of ASD in infants compared to the raw data set.

scholarly journals Discourse/Conversation Analysis and Autism Spectrum Disorder

2015 ◽  
Vol 46 (2) ◽  
pp. 355-359 ◽  
Author(s):  
Michelle O’Reilly ◽  
Jessica Nina Lester ◽  
Tom Muskett
Keyword(s):  
Autism Spectrum Disorder ◽  
Conversation Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder

scholarly journals Facilitating joint attention with salient pointing in interactions involving children with autism spectrum disorder

Gesture ◽  
2016 ◽  
Vol 15 (3) ◽  
pp. 372-403
Author(s):  
Katja Dindar ◽  
Terhi Korkiakangas ◽  
Aarno Laitila ◽  
Eija Kärnä
Keyword(s):  
Autism Spectrum Disorder ◽  
Conversation Analysis ◽  
Joint Attention ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Multimodal Approach ◽  
School Aged Children ◽  
Pointing Gestures ◽  
Pointing Gesture

Children with autism spectrum disorder (ASD) reportedly have difficulties in responding to bids for joint attention, notably in following pointing gestures. Previous studies have predominantly built on structured observation measures and predefined coding categories to measure children’s responsiveness to gestures. However, how these gestures are designed and what detailed interactional work they can accomplish have received less attention. In this paper, we use a multimodal approach to conversation analysis (CA) to investigate how educators design their use of pointing in interactions involving school-aged children with ASD or autistic features. The analysis shows that pointing had specific sequential implications for the children beyond mere attention sharing. Occasionally, the co-occurring talk and pointing led to ambiguities when a child was interpreting their interactional connotations, specifically when the pointing gesture lacked salience. The study demonstrates that the CA approach can increase understanding of how to facilitate the establishment of joint attention.

Brain morphometry volume in autistic spectrum disorder: a magnetic resonance imaging study of adults

2008 ◽  
Vol 39 (2) ◽  
pp. 337-346 ◽  
Author(s):  
B. Hallahan ◽  
E. M. Daly ◽  
G. McAlonan ◽  
E. Loth ◽  
F. Toal ◽  
...  
Keyword(s):  
Asperger's Syndrome ◽  
Asperger’S Syndrome ◽  
Imaging Study ◽  
Autism Spectrum ◽  
Small Sample ◽  
Spectrum Disorder ◽  
Head Size ◽  
Intracranial Volume ◽  
Age Related ◽  
Bulk Volume

BackgroundSeveral prior reports have found that some young children with autism spectrum disorder [ASD; including autism and Asperger's syndrome and pervasive developmental disorder – not otherwise specified (PDD-NOS)] have a significant increase in head size and brain weight. However, the findings from older children and adults with ASD are inconsistent. This may reflect the relatively small sample sizes that were studied, clinical heterogeneity, or age-related brain differences.MethodHence, we measured head size (intracranial volume), and the bulk volume of ventricular and peripheral cerebrospinal fluid (CSF), lobar brain, and cerebellum in 114 people with ASD and 60 controls aged between 18 and 58 years. The ASD sample included 80 people with Asperger's syndrome, 28 with autism and six with PDD-NOS.ResultsThere was no significant between-group difference in head and/or lobar brain matter volume. However, compared with controls, each ASD subgroup had a significantly smaller cerebellar volume, and a significantly larger volume of peripheral CSF.ConclusionsWithin ASD adults, the bulk volume of cerebellum is reduced irrespective of diagnostic subcategory. Also the significant increase in peripheral CSF may reflect differences in cortical maturation and/or ageing.

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